A stochastic model of streamflow for urbanized basins

Abstract

Given the critical role of the streamflow regime for instream, riparian, and floodplain ecosystem sustainability, modeling the long-term effect of urbanization on streamflow is important to predict possible changes in stream ecosystems. Since flow duration curves are largely used to characterize the streamflow regime and define indices for stream ecosystem health, we present two stochastic models, with different levels of complexity, that link the key physical features of urbanized basins with rainfall variability to determine the resulting flow duration curves. The two models are tested against 11 basins with various degrees of urban development, characterized by the percentage of impervious areas in the basin. Results show that the more complex model needs to be used to reproduce accurately the entire flow duration curve. The analysis performed suggests that the transformation of green (i.e., water used in evapotranspiration) to blue (i.e., streamflow) water in urbanized basins is an important long-term source of ecohydrological alteration. The modeling scheme also provides useful links between rainfall variability, urbanization levels, and some streamflow indices of high and low flows.

On the emergence of heavy-tailed streamflow distributions

“…Recently, the model has been extensively applied to characterize river flow regimes and a variety of biotic and anthropogenic riverine processes (see e.g. [7,8,16,19,26,28,29,33,34]). The ability of the model to reproduce observed probability distributions in the range of low to medium streamflows has been the object of previous studies [4,15].…”

Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors

“…Considering the focus of the IND on online (i.e., real time) applications, computationally efficient routines for parameter inference are important. For time series data, the likelihood function is given as a product of one-step-ahead conditional densities [Box et al, 2008;Madsen, 2008]. This approach is more efficient and easier to implement than sampling from the multivariate likelihood function when accounting for all the…”

“…Runoff modeling in urban hydrology distinguishes itself from its counterpart in natural catchment hydrology by the usually smaller temporal and spatial scales involved in peak discharge generation. Typical time steps for peak discharge simulations are 6 [Kleidorfer et al, 2009] to 15 min [Breinholt et al, 2012], but seconds [Freni et al, 2009] to days [Mej ıa et al, 2014] have been reported. Typical study areas of sewer watersheds range from dozens [Del Giudice et al, 2015] to more than 1000 ha [Breinholt et al, 2011].…”

Technical note: Long-term memory loss of urban streams as a metric for catchment classification

“…At the same time, the larger flows discharged in urban streams increase the export of nutrients and pollutants, and cause a decrease in biodiversity with associated detrimental effects to the health of riparian ecosystems (Walsh et al, 2005b). Although traditionally focused on peak flows, the management of urban stormwater runoff is now often directed toward restoration of the overall streamflow regime, which includes the dynamic range of streamflow fluctuations (see Mejía et al, 2014). Therefore, the management of urban streams 35 has been increasingly focused on controlling urban stormwater runoff with the intent to limit the streamflow during floods while also maintaining adequate flow rates during dry periods (Debusk et al, 2011;Burns et al, Hydrol.…”